import pandas as pd
import string
import matplotlib.pyplot as plt
import numpy as np
from wordcloud import WordCloud,STOPWORDS
from textblob import TextBlob
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.sentiment import SentimentIntensityAnalyzer
nltk.download('vader_lexicon')
nltk.download('punkt')
from collections import Counter
[nltk_data] Downloading package vader_lexicon to [nltk_data] C:\Users\USER\AppData\Roaming\nltk_data... [nltk_data] Package vader_lexicon is already up-to-date! [nltk_data] Downloading package punkt to [nltk_data] C:\Users\USER\AppData\Roaming\nltk_data... [nltk_data] Package punkt is already up-to-date!
df=pd.read_csv("C:/Users/USER/Downloads/Data Science Projects/mental_health.csv")
df.head()
| text | label | |
|---|---|---|
| 0 | dear american teens question dutch person hear... | 0 |
| 1 | nothing look forward lifei dont many reasons k... | 1 |
| 2 | music recommendations im looking expand playli... | 0 |
| 3 | im done trying feel betterthe reason im still ... | 1 |
| 4 | worried year old girl subject domestic physic... | 1 |
df['text']=df['text'].apply(str.lower)
df["text"] = df['text'].str.replace('[^\w\s]','')
C:\Users\USER\AppData\Local\Temp\ipykernel_9292\2390235465.py:2: FutureWarning: The default value of regex will change from True to False in a future version.
df["text"] = df['text'].str.replace('[^\w\s]','')
po_nltk=0
neg_nltk=0
neu_nltk=0
filtered=[]
for i in df['text']:
# Tokenization - breaking text into words
words = word_tokenize(i.lower()) # Convert to lowercase for consistency
# Add NLTK stopwords to the existing set
stopwords = set(STOPWORDS)
filtered_words = [word for word in words if word.isalnum() and word not in stopwords]
filtered.append(filtered_words)
sia = SentimentIntensityAnalyzer()
sentiment_score = sia.polarity_scores(' '.join(filtered_words))
if sentiment_score['compound']>=0.05:
po_nltk+=1
elif sentiment_score['compound']<=0.05:
neg_nltk+=1
else:
neu_nltk+=1
print(po_nltk, neg_nltk,neu_nltk)
11527 16450 0
flat_list = ' '.join([item for sublist in filtered for item in sublist])
flat_list
'